A weather radar shelter identification method

Abstract

Embodiments of the present disclosure provide a weather radar shelter identification method, which is applied to the technical field of meteorological radar data processing. The method comprises: acquiring radar station metadata information, digital elevation data, and weather radar base data collected within a continuous preset time period; identifying a terrain sheltered area within a detection range of the radar station according to the radar station metadata information and the digital elevation data, and forming an effective data set; determining a plurality of reference areas within the detection range that have been confirmed to have no shelter, determining a determination threshold value for representing a normal reflection level according to a reflectivity value of a corresponding reference area in the effective data set; dividing a potential non-terrain sheltered area within the detection range into a plurality of evaluation units, and using the reflectivity value of a corresponding evaluation unit in the effective data set and the determination threshold value to mark an evaluation unit that meets a preset abnormal condition as a non-terrain sheltered candidate area. In this way, terrain shelter and non-terrain shelter within the detection range of the weather radar can be accurately identified.

Description

Technical Field

[0001] This disclosure relates to the field of weather radar data processing technology, and in particular to a weather radar obstruction identification method. Background Technology

[0002] In actual operation of weather radar, the radar beam may be blocked by terrain undulations or man-made structures along its propagation path, resulting in weak or even completely absent detection echo intensity, which seriously affects the accuracy of subsequent applications such as quantitative precipitation estimation and severe convection monitoring.

[0003] Currently, methods for identifying terrain occlusion are relatively mature, mainly calculating the occlusion rate of beams in each direction using digital elevation models and radar beam geometry to generate mixed scanning elevation angles. However, these methods only reflect the impact of static terrain and cannot address permanent non-terrain occlusions such as high-rise buildings and industrial plants that are constantly emerging during urbanization. In addition, the industry has also developed non-meteorological echo identification methods based on radar base data echo characteristics. For example, they use physical features such as reflectivity texture and radial gradient to classify ground clutter, or detect abnormal azimuths by statistical deviation of the mean radial reflectivity under clear sky conditions. These methods focus on echo type discrimination and have not built a complete technical link from terrain occlusion removal to non-terrain occlusion thematic identification. This makes the identification results of non-terrain occlusions susceptible to interference from terrain occlusion signals, and lacks a mechanism to confirm the attributes (permanent or temporary) of occlusion objects, resulting in missed and false positives in actual network radar data quality control. Summary of the Invention

[0004] This disclosure provides a weather radar occlusion identification method, which solves the technical problem in the prior art that the separation of terrain occlusion identification and non-terrain occlusion identification makes the non-terrain occlusion identification results susceptible to interference from terrain occlusion signals and lacks a mechanism for confirming the attributes of occluded objects, thus making it difficult to systematically obtain accurate and reliable full occlusion spatial masks.

[0005] According to a first aspect of this disclosure, a method for identifying weather radar obstruction is provided. The method includes: Acquire radar station metadata, digital elevation data, and weather radar base data collected over a continuous preset time period; Based on the radar station metadata information and the digital elevation data, identify the terrain obstruction area within the radar station's detection range and generate a terrain obstruction spatial mask. Based on the terrain occlusion spatial mask, data corresponding to the terrain occlusion area are removed from the weather radar base data to form a valid dataset. Multiple confirmed unobstructed reference areas are identified within the detection range. Based on the reflectance values ​​of the corresponding reference areas in the valid dataset, a judgment threshold for characterizing the normal reflectance level is determined. The potential non-terrain occlusion area within the detection range is divided into multiple evaluation units. Using the reflectivity value of the evaluation unit corresponding to the effective dataset and the judgment threshold, the evaluation unit that meets the preset anomaly conditions is marked as a non-terrain occlusion candidate area.

[0006] In addition to the aspects described above and any possible implementation, a further implementation is provided in which the radar station metadata information includes the radar station's latitude and longitude coordinates, radar station feed height, radar beam elevation angle, radar beam vertical angle, and radar station's maximum detection range.

[0007] In addition to the aspects and any possible implementations described above, a further implementation is provided in which identifying terrain-obstructed areas within the detection range of the radar station and generating a terrain-obstructed spatial mask includes: Multiple target points are set within the maximum detection range of the radar station; For each target point, the radar beam center height at that target point is calculated based on the radar station feed height, the radar beam elevation angle, and the radial distance between the target point and the radar station. Based on the radial distance and the vertical angle of the radar beam, the vertical width of the radar beam at the target point is calculated, and half of the vertical width of the radar beam is subtracted from the center height of the radar beam to obtain the lower edge height of the radar beam. Obtain the terrain elevation at the target point; When the terrain elevation is higher than the lower edge of the radar beam, it is determined that the target point is obstructed by terrain. Based on the terrain occlusion determination results of each target point, a terrain occlusion spatial mask is generated.

[0008] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein generating a terrain occlusion spatial mask based on the terrain occlusion determination results of each target point includes: Along the detection radial direction of each radar, the target points are set as sampling points at preset radial intervals; For each radar detection radius, the ratio of the number of sampling points determined to have terrain obstruction to the total number of sampling points in that radius is used as the terrain obstruction rate of that radar detection radius. Based on the occlusion determination results of each target point and the terrain occlusion rate of each radar detection radial direction, a terrain occlusion spatial mask is generated within the maximum detection range of the radar station.

[0009] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein removing data corresponding to the terrain-obstructed area from the weather radar base data based on the terrain-obstructed spatial mask to form a valid dataset includes: Using the terrain occlusion spatial mask, spatial location matching is performed on the radar base dataset to remove all data that fall into the terrain occlusion area marked by the terrain occlusion spatial mask; The retained observation data will be used as the effective dataset corresponding to the potential non-terrain-shaded areas.

[0010] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein determining a plurality of confirmed unobstructed reference regions within the detection range, and determining a judgment threshold for characterizing normal reflectance levels based on the reflectance values ​​of the corresponding reference regions in the valid dataset, includes: For each reference region, the mean reflectance and standard deviation of reflectance for that reference region are calculated using the reflectance values ​​corresponding to that reference region in the effective dataset. Calculate the average of the reflectance mean values ​​of all reference regions as the global reference mean, and calculate the average of the standard deviations of the reflectance of all reference regions as the global reference standard deviation; The judgment threshold is obtained by subtracting the product of the preset coefficient and the global reference standard deviation from the global reference mean.

[0011] In addition to the aspects and any possible implementations described above, a further implementation is provided in which dividing the potential non-terrain obstruction area within the detection range into multiple evaluation units includes: The potential non-terrain occlusion area is divided into multiple grids with a preset azimuth step size and a preset radial distance step size, and each grid is used as an evaluation unit.

[0012] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the step of analyzing the reflectivity anomalies of each evaluation unit using the effective dataset and the determination threshold, and marking evaluation units that meet preset anomaly conditions as non-terrain occlusion candidate regions includes: For each evaluation unit, the total number of times the evaluation unit should be scanned by radar within the continuous preset time period is obtained as the theoretical total number of observations; Based on the valid dataset, determine the number of observations in which the evaluation unit actually has a valid reflectance value, and the number of observations in which the reflectance value is lower than the determination threshold; Calculate a first ratio of the number of observations with reflectance values ​​below the determination threshold to the number of actual observations with effective reflectance values, and a second ratio of the difference between the total theoretical observations and the number of actual observations with effective reflectance values ​​to the total theoretical observations. Evaluation units whose first ratio exceeds a preset first threshold, or evaluation units whose second ratio exceeds a preset second threshold, are marked as non-terrain occlusion candidate regions.

[0013] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes: The candidate regions without terrain occlusion are verified by field attributes. Candidate regions caused by temporary obstacles are excluded, and the remaining candidate regions are determined as the final non-terrain occlusion regions.

[0014] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the on-site attribute verification of the non-terrain occlusion candidate regions, excluding candidate regions caused by temporary obstacles, includes: Based on the azimuth and radial distance of the non-terrain-obstructed candidate area relative to the radar station, and combined with the latitude and longitude coordinates of the radar station, the actual spatial range corresponding to the candidate area is determined. The obstacles existing within the actual space area are verified on-site. When it is confirmed that the obstacle is a temporary structure or seasonal vegetation, the corresponding candidate area is excluded. When the obstacle is confirmed to be a permanent structure, the corresponding candidate area is retained, and the position, height, azimuth and radial distance of the permanent structure relative to the radar station are recorded to form an attribute ledger of the non-terrain obstruction area.

[0015] According to a second aspect of this disclosure, a weather radar obstruction identification device is provided. The device includes: The data acquisition module is used to acquire radar station metadata information, digital elevation data, and weather radar base data collected within a continuous preset time period; The terrain occlusion recognition module is used to identify terrain occlusion areas within the radar station's detection range based on the radar station's metadata information and the digital elevation data, and to generate a terrain occlusion spatial mask. The data removal module is used to remove data corresponding to the terrain-obstructed area from the weather radar base data based on the terrain obstruction spatial mask, thereby forming a valid dataset. The threshold determination module is used to determine multiple confirmed unobstructed reference areas within the detection range, and to determine a judgment threshold for characterizing the normal reflection level based on the reflectance value of the corresponding reference area in the effective data set. The non-terrain occlusion marking module is used to divide the potential non-terrain occlusion area within the detection range into multiple evaluation units. Using the reflectivity value of the evaluation unit corresponding to the effective dataset and the judgment threshold, the evaluation unit that meets the preset abnormal conditions is marked as a non-terrain occlusion candidate area.

[0016] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0017] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the methods according to the first and / or second aspects of this disclosure.

[0018] In this disclosure, firstly, by acquiring radar station metadata, digital elevation data, and long-sequence base data, terrain occlusion is identified and a spatial mask is generated using the metadata and digital elevation data, thereby clearly dividing the detection area into terrain-occluded areas and potentially non-terrain-occluded areas. Subsequently, the terrain occlusion spatial mask is used to remove data corresponding to terrain occlusion from the base data, forming a valid dataset reflecting only potentially non-terrain-occluded areas. This operation eliminates the interference of terrain occlusion factors from the data source, ensuring that subsequent statistical analysis of non-terrain occlusion is based on clean data. Next, several confirmed unoccluded reference areas are selected within the detection range. A threshold representing the normal reflectance level is calculated based on the corresponding reflectance values ​​in the valid dataset. This threshold can dynamically adapt to the climatic background and echo characteristics of different radar stations. Furthermore, the potentially non-terrain-occluded areas are divided into multiple evaluation units. Using the valid dataset and the threshold, evaluation units with abnormally low reflectance and statistical significance are marked as candidate non-terrain-occluded areas. This process, through multi-time, long-sequence statistical regularity analysis, effectively distinguishes between persistent echo attenuation or absence caused by permanent obstructions such as tall buildings and random fluctuations in weather processes, significantly improving the identification accuracy of non-topographic obstruction. Furthermore, by verifying on-site attributes to exclude temporary structures or seasonal vegetation, it ensures that the finally identified non-topographic obstruction areas are all man-made structures with long-term impacts, providing accurate and reliable spatial obstruction masks for radar base data quality labeling and subsequent quantitative applications. The overall technical solution forms a complete technical closed loop of "topographic obstruction identification → topographic obstruction data removal → reference normal field construction → non-topographic obstruction statistical identification → on-site confirmation," achieving systematic identification of all obstruction factors for weather radar, effectively improving radar data quality, and possessing good operational feasibility and prospects for widespread application.

[0019] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0020] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 A flowchart of a weather radar obstruction identification method provided by an embodiment of this disclosure is shown; Figure 2 A structural diagram of a weather radar obstruction identification device provided in an embodiment of this disclosure is shown; Figure 3 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0022] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0023] In this disclosure, firstly, by acquiring radar station metadata, digital elevation data, and long-sequence base data, terrain occlusion is identified and a spatial mask is generated using the metadata and digital elevation data, thereby clearly dividing the detection area into terrain-occluded areas and potentially non-terrain-occluded areas. Subsequently, the terrain occlusion spatial mask is used to remove data corresponding to terrain occlusion from the base data, forming a valid dataset reflecting only potentially non-terrain-occluded areas. This operation eliminates the interference of terrain occlusion factors from the data source, ensuring that subsequent statistical analysis of non-terrain occlusion is based on clean data. Next, several confirmed unoccluded reference areas are selected within the detection range. A threshold representing the normal reflectance level is calculated based on the corresponding reflectance values ​​in the valid dataset. This threshold can dynamically adapt to the climatic background and echo characteristics of different radar stations. Furthermore, the potentially non-terrain-occluded areas are divided into multiple evaluation units. Using the valid dataset and the threshold, evaluation units with abnormally low reflectance and statistical significance are marked as candidate non-terrain-occluded areas. This process, through multi-time, long-sequence statistical regularity analysis, effectively distinguishes between persistent echo attenuation or absence caused by permanent obstructions such as tall buildings and random fluctuations in weather processes, significantly improving the identification accuracy of non-topographic obstruction. Furthermore, by verifying on-site attributes to exclude temporary structures or seasonal vegetation, it ensures that the finally identified non-topographic obstruction areas are all man-made structures with long-term impacts, providing accurate and reliable spatial obstruction masks for radar base data quality labeling and subsequent quantitative applications. The overall technical solution forms a complete technical closed loop of "topographic obstruction identification → topographic obstruction data removal → reference normal field construction → non-topographic obstruction statistical identification → on-site confirmation," achieving systematic identification of all obstruction factors for weather radar, effectively improving radar data quality, and possessing good operational feasibility and prospects for widespread application.

[0024] Figure 1 A flowchart of a weather radar obstruction identification method provided by an embodiment of this disclosure is shown, such as... Figure 1 As shown, a weather radar obstruction identification method 100 may include the following steps: S110 acquires radar station metadata, digital elevation data, and weather radar base data collected over a continuous preset time period.

[0025] In some embodiments, the radar station metadata includes the radar station's latitude and longitude coordinates, radar station feed height, radar beam elevation angle, radar beam vertical angle, and radar station's maximum detection range.

[0026] Specifically, the radar station's metadata information is first retrieved from the radar station's operation and maintenance system or configuration file. This metadata should at least include the radar station's latitude and longitude coordinates, feed height, radar beam elevation angle, radar beam vertical angle, and the radar station's maximum detection range. Feed height refers to the height of the radar antenna feed relative to the ground; this parameter, along with the beam elevation angle, determines the initial emission geometry of the radar beam. The beam elevation angle can be any of several preset volume sweep elevation angles; in practical applications, the lowest elevation angle is often chosen to capture the earliest obstruction within a fully obstructed area. The radar beam vertical angle is the half-power beamwidth of the radar antenna in the vertical direction, determined by the radar hardware parameters. The maximum detection range of the radar station depends on the radar's transmit power and receiver sensitivity, and is typically the maximum radial distance available for a given reflectivity.

[0027] Specifically, the acquisition of digital elevation data must ensure that the spatial resolution and coverage meet the requirements of radar beam resolution and detection radius. Preferably, digital elevation model (DEM) data covering the radar station's detection range, such as SRTM, ASTER GDEM, or AW3D30, is downloaded from publicly available geospatial datasets. Multi-source DEM data are then stitched, projected, and resampled to a unified georeferenced grid that matches the radar polar coordinates. For areas with complex terrain, higher-resolution local DEMs can be used to supplement the data and improve the accuracy of beam obstruction detection.

[0028] Specifically, the selection of the data acquisition period for weather radar base data is crucial to ensuring the effectiveness of non-terrain obstruction statistical identification. The base data should cover a continuous, pre-defined period spanning multiple seasons and various typical weather types. Typically, volume scan base data spanning 3 to 6 months can be selected, ensuring the dataset includes diverse weather samples such as clear skies, stratiform cloud precipitation, convective precipitation, and mixed precipitation, so that the subsequently calculated reflectance statistical characteristics can comprehensively reflect the normal echo level under the climatic background of the radar station. During data acquisition, all volume scan files within the specified period are read in batches from the radar base data storage server according to time series. The reflectance values ​​for each range are extracted, and their corresponding timestamps, azimuth angles, and distance information are retained to construct a three-dimensional spatiotemporal data matrix.

[0029] Based on this, preliminary quality control can be performed on the acquired baseline data. Data from periods of instrument malfunction can be removed based on radar status codes, and abnormal paths caused by electromagnetic interference can be removed based on signal-to-noise ratio thresholds or radar constants, in order to reduce the contamination of subsequent statistical analysis by invalid data. Among these, the signal-to-noise ratio threshold... The setting can be based on the relationship between the minimum detectable echo power and the noise power, expressed as: in, To minimize the detectable echo power, This represents noise power. While this quality control step does not affect terrain occlusion identification, it directly determines the reliability of the effective dataset obtained after removing terrain occlusion data from the base data, providing a cleaner statistical sample for anomaly detection in non-terrain occlusion candidate areas. Through the above data acquisition and preprocessing steps, the basic data from geospatial static information to dynamic long-sequence observations are fully preserved. This provides necessary geometric parameters and elevation support for terrain occlusion geometric calculations, and also lays the data foundation for subsequent reference area reflectance statistical modeling and non-terrain occlusion anomaly identification. It enables terrain and non-terrain occlusion identification to share the same input data system, avoiding the shortcomings of data fragmentation and difficulty in coordination among various identification modules in traditional methods.

[0030] S120, based on the radar station metadata information and the digital elevation data, identify the terrain obstruction area within the radar station's detection range and generate a terrain obstruction spatial mask.

[0031] In some embodiments, identifying terrain-obstructed areas within the detection range of the radar station and generating a terrain-obstructed spatial mask includes: Multiple target points are set within the maximum detection range of the radar station; For each target point, the radar beam center height at that target point is calculated based on the radar station feed height, the radar beam elevation angle, and the radial distance between the target point and the radar station. Based on the radial distance and the vertical angle of the radar beam, the vertical width of the radar beam at the target point is calculated, and half of the vertical width of the radar beam is subtracted from the center height of the radar beam to obtain the lower edge height of the radar beam. Obtain the terrain elevation at the target point; When the terrain elevation is higher than the lower edge of the radar beam, it is determined that the target point is obstructed by terrain. Based on the terrain occlusion determination results of each target point, a terrain occlusion spatial mask is generated.

[0032] Specifically, the identification of terrain-obstructed areas relies on the spatial overlay analysis of the radar beam's geometric propagation model and digital elevation data. First, based on the radar station's metadata, a polar coordinate sampling grid is established within the radar station's maximum detection range. A series of target points are then set at preset radial intervals along each azimuth angle as sampling locations for obstruction determination. The selection of the radial interval must balance computational efficiency and identification accuracy, typically using a spacing comparable to or slightly coarser than the radar range database resolution to ensure that each target point can establish a clear spatial correspondence with the digital elevation data and subsequent base data grid.

[0033] For each target point, based on the equivalent Earth radius model under standard atmospheric refraction conditions, the center height of the radar beam at that point is calculated using the radial distance, radar beam elevation angle, and feed height. For any target point within the radar detection range, the formula for calculating the radar beam center height H under standard atmospheric refraction conditions is: in, The height of the radar station feed. The horizontal distance from the radar station to the target point. The equivalent Earth radius is taken as 8500 km under standard atmospheric conditions. Let be the radar beam elevation angle. This formula decomposes the beam center height into three parts: feed height, Earth curvature correction term, and vertical height caused by the elevation angle, thus fully reflecting the geometric propagation characteristics of the radar beam under standard atmospheric refraction conditions.

[0034] After obtaining the beam center height, the vertical beamwidth at the target point needs to be calculated to determine the actual spatial position of the lower edge of the beam. (Radar beam vertical width) The calculation formula is: in, The vertical beamwidth is the radar beam angle, which is the half-power beamwidth of the antenna in the vertical direction. This formula assumes that the beam widens in a conical shape under far-field conditions, and that the vertical width is proportional to the radial distance.

[0035] Based on the two calculation results above, subtracting half of the vertical width from the beam center height yields the lower edge height of the radar beam. : The lower edge height of the beam characterizes the lowest boundary of the radar beam energy distribution in the vertical direction, and is a direct parameter for determining whether terrain intrudes into the beam propagation path. This calculation process fully characterizes the spatial occupancy range of the radar beam at the target point, that is, the energy propagation range of the beam with the center height as the axis and the vertical width as the span.

[0036] Subsequently, the terrain elevation value at the target point is extracted from the pre-processed consistent georeferenced digital elevation data using a spatial location index. The digital elevation data needs to be pre-sampled to a spatial resolution matching the sampling grid to ensure the location accuracy of the elevation extraction. The logic for occlusion determination is as follows: when the terrain elevation at the target point is greater than the lower edge height of the radar beam, it indicates that the terrain intrudes into the space below the lower edge of the beam, effectively occluding the beam, and the target point is marked as a terrain-occluded point; conversely, if the terrain elevation is less than or equal to the lower edge height of the beam, it is determined to be unoccluded. This point-by-point determination process covers all sampled target points within the radar station's detection range, generating a full-range occlusion binarized marker map, where each target point carries a "occluded" or "unoccluded" status attribute.

[0037] In some embodiments, generating a terrain occlusion spatial mask based on the terrain occlusion determination results of each target point includes: Along the detection radial direction of each radar, the target points are set as sampling points at preset radial intervals; For each radar detection radius, the ratio of the number of sampling points determined to have terrain obstruction to the total number of sampling points in that radius is used as the terrain obstruction rate of that radar detection radius. Based on the occlusion determination results of each target point and the terrain occlusion rate of each radar detection radial direction, a terrain occlusion spatial mask is generated within the maximum detection range of the radar station.

[0038] Specifically, based on the point-by-point occlusion determination, radial-dimensional statistics are further introduced to generate a terrain occlusion spatial mask, reflecting the overall degree of occlusion in different directions. Target points are evenly distributed at preset intervals along each radar detection radial direction, and these target points simultaneously serve as sampling points for that radial direction. For each radial direction, the number of sampling points determined to have terrain occlusion is counted, and the ratio is calculated to the total number of sampling points to obtain the terrain occlusion rate for that radial direction. The terrain occlusion rate is a quantitative indicator between 0 and 1, intuitively representing the severity of terrain occlusion on the beam propagation path in that azimuth. A higher value indicates denser occlusion along the line, and a more significant limitation on radar detection capability. Combining the occlusion status of each target point with the occlusion rate of each radial direction forms a complete terrain occlusion spatial mask. This mask contains information at both the pixel-level occlusion marker and radial-level statistics levels, providing a spatially clear and statistically distinct representation of terrain occlusion knowledge for subsequent steps.

[0039] S130, based on the terrain occlusion spatial mask, remove the data corresponding to the terrain occlusion area from the weather radar base data to form a valid dataset.

[0040] In some embodiments, the step of removing data corresponding to the terrain-obstructed area from the weather radar base data based on the terrain-obstructed spatial mask to form a valid dataset includes: Using the terrain occlusion spatial mask, spatial location matching is performed on the radar base dataset to remove all data that fall into the terrain occlusion area marked by the terrain occlusion spatial mask; The retained observation data will be used as the effective dataset corresponding to the potential non-terrain-shaded areas.

[0041] Specifically, the terrain occlusion spatial mask is first loaded into a georeferenced frame unified with the radar base data. The weather radar base data, after preprocessing, is organized according to a three-dimensional structure of "time-azimuth-range," with each range database having a defined azimuth and radial distance. Each target point in the terrain occlusion spatial mask also corresponds to a unique combination of azimuth and radial distance. Therefore, by traversing each range database in the base data and searching for the corresponding target point's occlusion marker in the mask based on its azimuth and distance, a one-to-one spatial match can be achieved. All data falling within the terrain occlusion area after matching, regardless of their reflectivity or observation time, are completely removed and not included in any subsequent statistical analysis. The rationale for this "complete removal" strategy is that the beam obstruction caused by terrain occlusion is a permanent physical obstruction. The echo intensity recorded in the obstructed area is not the backscattering of the actual meteorological target, but rather the residue of noise or ground clutter, completely lacking the ability to reflect atmospheric conditions. Retaining this data would only pollute subsequent statistical models.

[0042] Specifically, after the removal process, the remaining portion of the radar base data corresponds to the observation data for potential non-terrain-obstructed areas. This data covers areas where the terrain is not obstructed and, theoretically, the radar beam can penetrate without obstruction. This retained data is then reorganized to form a valid dataset. This dataset inherits the temporal continuity of the original base data and includes reflectivity observations for all available time intervals and elevation angles within a continuous preset time period in potential non-terrain-obstructed areas. The completion of this data purification process signifies that the identification and elimination of terrain-obstructing factors have been fully achieved, and the entire technical solution has officially entered the stage of specialized identification of non-terrain-obstructing factors.

[0043] S140, determine multiple confirmed unobstructed reference areas within the detection range, and determine a judgment threshold for characterizing the normal reflection level based on the reflectivity values ​​of the corresponding reference areas in the valid data set.

[0044] In some embodiments, determining a plurality of confirmed unobstructed reference regions within the detection range, and determining a threshold for characterizing normal reflectance levels based on the reflectance values ​​of the corresponding reference regions in the valid dataset, includes: For each reference region, the mean reflectance and standard deviation of reflectance for that reference region are calculated using the reflectance values ​​corresponding to that reference region in the effective dataset. Calculate the average of the reflectance mean values ​​of all reference regions as the global reference mean, and calculate the average of the standard deviations of the reflectance of all reference regions as the global reference standard deviation; The judgment threshold is obtained by subtracting the product of the preset coefficient and the global reference standard deviation from the global reference mean.

[0045] Specifically, each selected reference area should be located in a region where the terrain occlusion identification result indicates no obstruction, the terrain is open, and it is far from urban building clusters and industrial facilities. Its coverage should span at least several consecutive radar radials in azimuth and cover a sufficiently long radial interval in distance to ensure a sufficient sample size and stable statistical results. These reference areas should be spatially distributed across different quadrants of the radar station's detection range to avoid statistical biases caused by systematic differences in local microclimates.

[0046] Specifically, reflectance values ​​are extracted from the valid dataset according to the spatial extent of each reference area. Only observations from the valid dataset are used during extraction; these are data that have been excluded from terrain shading interference and have passed preliminary quality control. For a single reference area, the effective reflectance values ​​at all times and all elevation angles within that area are used as a statistical set, and their mean and standard deviation are calculated. The mean quantitatively reflects the average echo level of the area under normal weather and clear-sky conditions over a continuous preset time period, while the standard deviation measures the natural amplitude of reflectance fluctuations due to weather processes and seasonal variations.

[0047] Based on this, the mean values ​​of all reference areas are averaged to obtain the global reference mean; the standard deviations of all reference areas are averaged to obtain the global reference standard deviation. This averaging operation eliminates potential local anomalies or micro-meteorological features in individual reference areas, making the statistics more spatially representative. Subsequently, the product of a preset coefficient and the global reference standard deviation is subtracted from the global reference mean to obtain the judgment threshold, which represents the lower bound of the statistically normal reflectance level. When the reflectance at a certain location is consistently below this threshold, it indicates that its echo intensity deviates significantly from the normal variation range of similar spaces under unobstructed conditions, and there is reason to infer that it has been affected by additional non-meteorological attenuation factors. Thus, through four closely linked steps—reference area selection, single-area statistics, global synthesis, and threshold calculation—a seamless transformation from clean base data to anomaly judgment criteria is completed.

[0048] S150, the potential non-terrain occlusion area within the detection range is divided into multiple evaluation units. Using the reflectivity value of the evaluation unit corresponding to the effective dataset and the judgment threshold, the evaluation unit that meets the preset abnormal conditions is marked as a non-terrain occlusion candidate area.

[0049] In some embodiments, dividing the potential non-terrain obstruction area within the detection range into multiple evaluation units includes: The potential non-terrain occlusion area is divided into multiple grids with a preset azimuth step size and a preset radial distance step size, and each grid is used as an evaluation unit.

[0050] Specifically, the grid division method adopts a regular grid generation strategy in polar coordinates. Centered on the radar station, the potentially non-terrain-obstructed area is divided into several continuous two-dimensional sector grids with a preset azimuth step size and a preset radial distance step size. Each grid is an independent evaluation unit. The azimuth and radial distance step sizes must balance the sufficiency of the statistical sample size with the fineness of spatial resolution, typically maintaining coordination with the inherent azimuth resolution and range database resolution of the radar base data. This ensures that each evaluation unit can accumulate sufficient observation samples throughout the entire volume scan over a continuous preset time period. After grid division, each evaluation unit has definite azimuth and radial distance boundaries, providing a spatial index for the subsequent accurate extraction of corresponding reflectivity values ​​from the effective dataset.

[0051] In some embodiments, the step of analyzing reflectivity anomalies in each evaluation unit using the effective dataset and the determination threshold, and marking evaluation units that meet preset anomaly conditions as non-terrain occlusion candidate regions includes: For each evaluation unit, the total number of times the evaluation unit should be scanned by radar within the continuous preset time period is obtained as the theoretical total number of observations; Based on the valid dataset, determine the number of observations in which the evaluation unit actually has a valid reflectance value, and the number of observations in which the reflectance value is lower than the determination threshold; Calculate a first ratio of the number of observations with reflectance values ​​below the determination threshold to the number of actual observations with effective reflectance values, and a second ratio of the difference between the total theoretical observations and the number of actual observations with effective reflectance values ​​to the total theoretical observations. Evaluation units whose first ratio exceeds a preset first threshold, or evaluation units whose second ratio exceeds a preset second threshold, are marked as non-terrain occlusion candidate regions.

[0052] Specifically, for each evaluation unit, the determination of anomalies does not rely on the echo performance at a single moment, but is based on the long-sequence statistical regularity of all valid observations within a continuous preset time period. First, based on the radar's volume scan mode parameters and the time span of the continuous preset time period, the total number of times the evaluation unit should theoretically be scanned by the radar is calculated as the theoretical total number of observations. The theoretical total number of observations represents the upper limit of the reflectivity observation sample size that the unit should obtain under ideal conditions where the radar has no obstruction and the data is received completely. Subsequently, based on the valid dataset, the number of observations in which the evaluation unit actually has valid reflectivity values ​​in the actual records is counted, as well as the number of observations among these valid observations where the reflectivity value is lower than the judgment threshold obtained by S140. Here, it is necessary to clarify two levels of statistics: the first level measures the situation of complete signal loss from the theoretical observation level, and the second level measures the situation of abnormal signal strength attenuation from the valid observation level.

[0053] Specifically, if there are non-terrain obstructions at the location of the assessment unit, the obstruction effect typically manifests in two observable anomalies in the radar base data. The first is a complete absence of reflectivity echoes, meaning that the radar-transmitted pulses are blocked by the obstruction and cannot reach the subsequent meteorological targets, resulting in no effective echo records for the assessment unit for a large number of time periods. The second is an abnormally low reflectivity value, meaning that although some energy from the beam still reaches the assessment unit due to diffraction or bend effects, the echo intensity has significantly weakened to a level that should not occur under normal weather conditions. To capture these two anomalies separately, two statistical ratios are calculated: the first ratio is obtained by comparing the number of observations with reflectivity values ​​below the judgment threshold to the number of observations with effective reflectivity values. This ratio reflects the frequency at which the echo is abnormally weakened when the radar can receive the echo; the second ratio is obtained by comparing the difference between the total theoretical observations and the number of observations with effective reflectivity values ​​to the total theoretical observations. This second ratio directly characterizes the proportion of time data is missing due to obstruction. The first and second thresholds are preset as the upper bound criteria for these two statistical indicators. If either ratio exceeds its corresponding threshold, it is sufficient to indicate that the reflectivity of the evaluation unit exhibits a continuous and systematic abnormality in a statistical sense, which cannot be explained by short-term weather phenomena. Therefore, it is marked as a candidate area for non-terrain shading.

[0054] In some embodiments, the method further includes: The candidate regions without terrain occlusion are verified by field attributes. Candidate regions caused by temporary obstacles are excluded, and the remaining candidate regions are determined as the final non-terrain occlusion regions.

[0055] In some embodiments, performing on-site attribute verification on the non-terrain occlusion candidate regions to exclude candidate regions caused by temporary obstacles includes: Based on the azimuth and radial distance of the non-terrain-obstructed candidate area relative to the radar station, and combined with the latitude and longitude coordinates of the radar station, the actual spatial range corresponding to the candidate area is determined. The obstacles existing within the actual space area are verified on-site. When it is confirmed that the obstacle is a temporary structure or seasonal vegetation, the corresponding candidate area is excluded. When the obstacle is confirmed to be a permanent structure, the corresponding candidate area is retained, and the position, height, azimuth and radial distance of the permanent structure relative to the radar station are recorded to form an attribute ledger of the non-terrain obstruction area.

[0056] Specifically, after the automatic marking of candidate areas, a field attribute verification step was introduced to distinguish between permanent man-made structures and temporary obstacles or seasonal vegetation, ensuring that the finally determined non-terrain-obstructed areas all have a long-term stable obstruction effect. For each marked candidate area, based on its azimuth and radial distance relative to the radar station, combined with the radar station's latitude and longitude coordinates obtained in S110, its polar coordinate position was converted to geodetic latitude and longitude through coordinate forward calculation, thereby accurately defining the corresponding field extent of the candidate area in geographic space. A field survey was conducted for each field extent to verify the types of obstacles present. If the obstacles are temporary or periodic objects such as temporary tower cranes at construction sites, temporary material stockpiles, or large patches of seasonally growing tall vegetation, their obstruction effect on the radar beam is not long-term and will be excluded from the candidate areas. If an obstacle is confirmed to be a permanent structure such as a high-rise building, a fixed industrial plant, or a permanent steel structure tower, its corresponding candidate area is retained, it is classified as the final non-terrain obstruction area, and the geographical location, top height, and azimuth and radial distance of the permanent structure relative to the radar station are recorded in the system to form an attribute ledger.

[0057] Thus, the entire technical solution forms a complete and closed-loop system for identifying weather radar full occlusion factors, from multi-source data acquisition, geometric identification and removal of terrain occlusion, establishment of normal reflection benchmarks, statistical identification of non-terrain occlusion, to on-site attribute confirmation. The generated terrain and non-terrain occlusion spatial masks can be directly used for data quality control in operational processes such as radar quantitative precipitation estimation and severe weather monitoring.

[0058] According to the embodiments of this disclosure, the following technical effects are achieved: (1) A systematic identification of terrain and non-terrain occlusion is realized: by first using digital elevation data and beam geometry model to identify terrain occlusion and generate spatial mask, and then using long sequence base data statistical features to identify non-terrain occlusion, a complete occlusion identification technology closed loop from terrain to non-terrain and from static to dynamic is formed, which solves the problem of the separation and difficulty in coordination of the two types of occlusion in traditional methods.

[0059] (2) Improved the accuracy of non-terrain occlusion identification: Before identifying non-terrain occlusion, the terrain occlusion spatial mask is used to remove all data of the terrain occlusion area from the base data, so that the subsequent statistical analysis is based on a clean and effective dataset, eliminating the interference of terrain occlusion signal from the source, and avoiding the misjudgment of the missing or weak echo caused by terrain occlusion as non-terrain occlusion.

[0060] (3) A dynamic anomaly judgment benchmark was established: by selecting multiple unobstructed reference areas within the detection range, calculating the mean and standard deviation of reflectivity in each area, and then synthesizing the global reference mean and global reference standard deviation, and using this to determine the threshold for abnormally low reflectivity, the judgment benchmark can adapt to the climate background and echo characteristics of different radar stations, avoiding the applicability problem of fixed thresholds in different geographical environments.

[0061] (4) The statistical significance of occlusion anomalies was determined: the first ratio and the second ratio were used as two statistical indicators to characterize the continuous impact of non-terrain occlusion from the two dimensions of abnormally low reflectance and missing data. The determination was based on the long-sequence statistical regularity of continuous preset time periods, which effectively distinguished the systematic deviation caused by permanent occlusion from the random fluctuation of weather processes and reduced the misjudgment rate.

[0062] (5) The reliability of the shading properties was ensured through field verification: the candidate areas marked by automatic coordinate calculation and on-site investigation were carried out to distinguish between permanent structures and temporary structures and seasonal vegetation, and to eliminate the interference of temporary shading factors. This ensured that the finally determined non-terrain shading areas all had a long-term stable shading effect and formed an attribute ledger containing information such as location, height, azimuth-distance relationship.

[0063] (6) Improved radar data quality and reliability of quantitative applications: The generated terrain and non-terrain occlusion spatial masks can be directly used for radar base data quality control, providing accurate spatial occlusion labeling for downstream services such as quantitative precipitation estimation and severe convection monitoring, effectively improving the accuracy of radar network mosaic and quantitative inversion products.

[0064] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0065] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0066] Figure 2 A structural diagram of a weather radar obstruction identification device provided in an embodiment of this disclosure is shown, such as... Figure 2 As shown, a weather radar obstruction identification device 200 may include: The data acquisition module 210 is used to acquire radar station metadata information, digital elevation data, and weather radar baseline data collected within a continuous preset time period.

[0067] The terrain occlusion recognition module 220 is used to identify terrain occlusion areas within the radar station's detection range based on the radar station's metadata information and the digital elevation data, and to generate a terrain occlusion spatial mask.

[0068] The data removal module 230 is used to remove data corresponding to the terrain-obstructed area from the weather radar base data based on the terrain obstruction spatial mask, thereby forming a valid dataset.

[0069] The threshold determination module 240 is used to determine multiple confirmed unobstructed reference areas within the detection range, and to determine a judgment threshold for characterizing the normal reflection level based on the reflectance value of the corresponding reference area in the effective data set.

[0070] The non-terrain occlusion marking module 250 is used to divide the potential non-terrain occlusion area within the detection range into multiple evaluation units, and use the reflectivity value of the evaluation unit corresponding to the effective dataset and the judgment threshold to mark the evaluation unit that meets the preset abnormal conditions as a non-terrain occlusion candidate area.

[0071] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0072] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0073] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0074] Figure 3A schematic block diagram of an electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0075] Electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in ROM 302 or a computer program loaded into RAM 303 from storage unit 308. RAM 303 can also store various programs and data required for the operation of electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O interface 305 is also connected to bus 304.

[0076] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0077] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).

[0078] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0079] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0080] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0081] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0082] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0083] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0084] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.

[0085] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for identifying weather radar obstruction, characterized in that, include: Acquire radar station metadata, digital elevation data, and weather radar base data collected over a continuous preset time period; Based on the radar station metadata information and the digital elevation data, identify the terrain obstruction area within the radar station's detection range and generate a terrain obstruction spatial mask. Using the terrain occlusion spatial mask, spatial location matching is performed on the weather radar base data, and all data falling into the terrain occlusion area marked by the terrain occlusion spatial mask are removed; the retained observation data is used as the effective dataset corresponding to the potential non-terrain occlusion area. Multiple confirmed unobstructed reference areas are identified within the detection range. For each reference area, the reflectance mean and standard deviation of the reflectance are calculated using the reflectance values ​​corresponding to that reference area from the effective data set. The average of the reflectance mean values ​​of all reference areas is calculated as the global reference mean, and the average of the reflectance standard deviations of all reference areas is calculated as the global reference standard deviation. The judgment threshold is obtained by subtracting the product of the preset coefficient and the global reference standard deviation from the global reference mean. The potential non-terrain occlusion area within the detection range is divided into multiple evaluation units. Using the reflectivity value of the evaluation unit corresponding to the effective dataset and the judgment threshold, the evaluation unit that meets the preset anomaly conditions is marked as a non-terrain occlusion candidate area.

2. The method according to claim 1, characterized in that, The radar station metadata includes the radar station's latitude and longitude coordinates, radar station feed height, radar beam elevation angle, radar beam vertical angle, and radar station's maximum detection range.

3. The method according to claim 2, characterized in that, The process of identifying terrain-obstructed areas within the detection range of the radar station and generating a terrain-obstructed spatial mask includes: Multiple target points are set within the maximum detection range of the radar station; For each target point, the radar beam center height at that target point is calculated based on the radar station feed height, the radar beam elevation angle, and the radial distance between the target point and the radar station. Based on the radial distance and the vertical angle of the radar beam, the vertical width of the radar beam at the target point is calculated, and half of the vertical width of the radar beam is subtracted from the center height of the radar beam to obtain the lower edge height of the radar beam. Obtain the terrain elevation at the target point; When the terrain elevation is higher than the lower edge of the radar beam, it is determined that the target point is obstructed by terrain. Based on the terrain occlusion determination results of each target point, a terrain occlusion spatial mask is generated.

4. The method according to claim 3, characterized in that, The generation of the terrain occlusion spatial mask based on the terrain occlusion determination results of each target point includes: Along the detection radial direction of each radar, the target points are set as sampling points at preset radial intervals; For each radar detection radius, the ratio of the number of sampling points determined to have terrain obstruction to the total number of sampling points in that radius is used as the terrain obstruction rate of that radar detection radius. Based on the occlusion determination results of each target point and the terrain occlusion rate of each radar detection radial direction, a terrain occlusion spatial mask is generated within the maximum detection range of the radar station.

5. The method according to claim 1, characterized in that, The step of dividing the potential non-terrain obstruction area within the detection range into multiple evaluation units includes: The potential non-terrain occlusion area is divided into multiple grids with a preset azimuth step size and a preset radial distance step size, and each grid is used as an evaluation unit.

6. The method according to claim 1, characterized in that, The step of using the reflectivity value of the evaluation unit corresponding to the effective dataset and the determination threshold to mark the evaluation unit that meets the preset anomaly conditions as a non-terrain occlusion candidate region includes: For each evaluation unit, the total number of times the evaluation unit should be scanned by radar within the continuous preset time period is obtained as the theoretical total number of observations; Based on the valid dataset, determine the number of observations in which the evaluation unit actually has a valid reflectance value, and the number of observations in which the reflectance value is lower than the determination threshold; Calculate a first ratio of the number of observations with reflectance values ​​below the determination threshold to the number of actual observations with effective reflectance values, and a second ratio of the difference between the total theoretical observations and the number of actual observations with effective reflectance values ​​to the total theoretical observations. Evaluation units whose first ratio exceeds a preset first threshold, or evaluation units whose second ratio exceeds a preset second threshold, are marked as non-terrain occlusion candidate regions.

7. The method according to claim 2, characterized in that, The method further includes: The candidate regions without terrain occlusion are verified by field attributes. Candidate regions caused by temporary obstacles are excluded, and the remaining candidate regions are determined as the final non-terrain occlusion regions.

8. The method according to claim 7, characterized in that, The step of performing on-site attribute verification on the non-terrain-obstructed candidate regions and excluding candidate regions caused by temporary obstacles includes: Based on the azimuth and radial distance of the non-terrain-obstructed candidate area relative to the radar station, and combined with the latitude and longitude coordinates of the radar station, the actual spatial range corresponding to the candidate area is determined. The obstacles existing within the actual space area are verified on-site. When it is confirmed that the obstacle is a temporary structure or seasonal vegetation, the corresponding candidate area is excluded. When the obstacle is confirmed to be a permanent structure, the corresponding candidate area is retained, and the position, height, azimuth and radial distance of the permanent structure relative to the radar station are recorded to form an attribute ledger of the non-terrain obstruction area.

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